Guiding RTL Test Generation Using Relevant Potential Invariants
Tania Khanna, Michael S. Hsiao · 2018
We propose to use relevant potential invariants in a simulation-based swarm-intelligence test generation technique to generate efficient test vectors for design validation at the Register Transfer Level. These potential invariants are obtained using random stimuli such that they are true under these stimuli. Since these potential invariants are only likely to be true, we try to generate stimuli that can falsify them. Any such vector would help reach some corners of the design. However, the space of potential invariants can be extremely large. To reduce execution time, we implement a two-layer filter to remove the irrelevant potential invariants that may not contribute in reaching difficult states. With the filter, the vectors generated thus help to reduce the overall test length while still reach the same coverage as considering all unfiltered potential invariants. Experimental results show that with only the filtered potential invariants, we were able to reach equal or better branch coverage in the ITC99 benchmarks, with considerable reduction in vector lengths, at reduced execution time.